Percorrer por autor "Domingues, Patrício"
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- Active and Assisted Living Ecosystem for the ElderlyPublication . Marcelino, Isabel; Laza, Rosalía; Domingues, Patrício; Gómez-Meire, Silvana; Fdez-Riverola, Florentino; Pereira, AntónioA novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors.
- Decrypting messages: Extracting digital evidence from signal desktop for windowsPublication . Paulino, Gonçalo; Negrão, Miguel; Frade, Miguel; Domingues, PatrícioWith growing concerns over the security and privacy of personal conversations, end-to-end encrypted instant messaging applications have become a key focus of forensic research. This study presents a detailed methodology along with an automated Python script for decrypting and analyzing forensic artifacts from Signal Desktop for Windows. The methodology is divided into two phases: i) decryption of locally stored data and ii) analysis and documentation of forensic artifacts. To ensure data integrity, the proposed approach enables retrieval without launching Signal Desktop, preventing potential alterations. Additionally, a reporting module organizes extracted data for forensic investigators, enhancing usability. Our approach is effective in extracting and analyzing encrypted Signal artifacts, providing a reliable method for forensic investigations.
- Deep Learning-based Facial Detection and Recognition in Still Images for Digital ForensicsPublication . Domingues, Patrício; Rosário, Alexandre FrazãoSmartphones and cheap storage have contributed to a deluge of digital photos. Digital forensic analysis often include the need to process large volumes of digital photos found on devices. Sometimes, this is done either to detect or confirm the ownership of the device or to determine whether the owner of the device has some acquaintance of interest in the case. In this paper, we present the Face Detection and Recognition in Images (FDRI) open source software, and its integration as a module for the digital forensic software Autopsy. FDRI aims to semi-automate the detection of faces in digital photos, flagging photos where at least one face is detected. FDRI software also performs face recognition, searching for the existence of given individual(s) in still photos of the forensically examined devices. For both the detection and recognition of faces, FDRI resorts to deep learning-based algorithms available within the dlib machine learning toolkit. In experimental assessments, FDRI yielded an average precision of 99.46% face detection and 98.10% for face recognition, when dealing with the restrained LFW dataset. For unrestrained real world photos, FDRI achieved a precision of 97.67% for face detection and 81.82% for face recognition. Performance-wise, this study confirms the importance of a fast GPU for fast face detection and recognition, with an NVidia GTX 1070 being roughly three times faster than a GTX 750 Ti, and in certain cases, up to 35× faster than the CPU version.
- Digital Forensic Artifacts of the Cortana Device Search Cache on Windows 10 DesktopPublication . Domingues, Patrício; Frade, MiguelMicrosoft Windows 10 Desktop edition has brought some new features and updated other ones that are of special interest to digital forensics analysis. The search box available on the taskbar, next to the Windows start button is one of these novelties. Although the primary usage of this search box is to act as an interface to the intelligent personal digital assistant Cortana, in this paper, we study the digital forensic artifacts of the search box on machines when Cortana is explicitly disabled. Specifically, we locate, characterize and analyze the content and dynamics of the JSON-based files that are periodically generated by the Cortana device search cache system. Forensically important data from these JSON files include the number of times each installed application has been run, the date of the last execution and the content of the custom jump list of the applications. Since these data are collected per user and saved in a resilient text format, they can help in digital forensics, mostly in assisting the validation of other sources of information.
- Digital forensic artifacts of the Your Phone application in Windows 10Publication . Domingues, Patrício; Frade, Miguel; Andrade, Luis Miguel; Silva, João VictorYour Phone is a Microsoft system that comprises two applications: a smartphone app for Android 7 + smartphones and a desktop application for Windows 10/18.03+. It allows users to access their most recent smartphone-stored photos/screenshots and send/receive short message service (SMS) and multimedia messaging service (MMS) within their Your Phone-linked Windows 10 personal computers. In this paper, we analyze the digital forensic artifacts created at Windows 10 personal computers whose users have the Your Phone system installed and activated. Our results show that besides the most recent 25 photos/screenshots and the content of the last 30-day of sent/received SMS/MMS, the contact database of the linked smartphone(s) is available in a accessible SQLite3 database kept at the Windows 10 system. This way, when the linked smartphone cannot be forensically analyzed, data gathered through the Your Phone artifacts may constitute a valuable digital forensic asset. Furthermore, to explore and export the main data of the Your Phone database as well as recoverable deleted data, a set of python scripts – Your Phone Analyzer (YPA) – is presented. YPA is available wrapped within an Autopsy module to assist digital practitioners to extract the main artifacts from the Your Phone system.
- Digitally Signed and Permission Restricted PDF FilesPublication . Domingues, Patrício; Frade, MiguelThe PDF format is the de-facto standard for many types of documents. Often a forensic digital investigation is faced with a significant volume of PDF files. It is thus important to filter PDF files, giving priority to files that have an high probability to carry important and meaningful data. In this paper, we focus on identifying potential important PDF files, selecting i) digitally signed files and ii) files that have special owner restrictions set, such as interdiction to assemble/separate pages. For this purpose, we present the python-based digiSign|protectedPDF module for the open source Autopsy forensic software. When run over a digital forensic data source, the module creates two lists: one holding the digitally signed files and, another one with files that have special restrictions in their usage. To study the occurrence of digitally signed and of permission-protected PDF and their importance for digital forensics, we analyzed a Windows 10 forensic image, finding that 2.81% of the PDF files were digitally signed and 3.75% were permission-protected. The study shows that digitally signed PDF files can harbor meaningful data for a digital forensic investigation.
- Energy-Efficient and Portable Least Squares Prediction for Image Coding on a Mobile GPUPublication . Cordeiro, Pedro; Falcao, Gabriel; Domingues, Patrício; Rodrigues, Nuno; Faria, SergioLeast squares prediction is a technique used to foresee pixel values during image coding by finding the minimum square error of neighbouring pixels. It has shown considerable quality gains especially for complex images with high variations in pixel intensities. The drawback of this technique consists of high computational complexity, consuming the most significant part of processing time and resources available, which makes it difficult to implement in fast, lossy image coders. One challenge is therefore to reduce the computational time of this predictor, namely through the use of new parallel programming techniques, making it more attractive for state-of-the-art coder-decoders. Also, new algorithmic propositions are made, trying to reduce the time spent in exchange for rate-distortion performance. These propositions are senseful since this predictor is used not only in lossless image coding, but also in lossy as well. Another aim of this article is to analyze energy efficiency among different types of platforms for this signal processing algorithm. Comparisons are provided on parallel computing processors ranging from very powerful Graphics Computing Units (GPUs) to mobile General-Purpose GPUs.
- eServices - Service Platform for Pervasive Elderly CarePublication . Marcelino, Isabel; Laza, Rosalía; Domingues, Patrício; Gómez-Meire, Silvana; Pereira, AntónioIn this paper, we present a solution to improve elderly’s quality of life. eServices – Service platform for pervasive elderly care was designed to aggregate several services developed to meet senior population’s needs. It monitors basic life signs, environment variables and uses personal location technology. Besides sensor services, eServices solution contains digital services align with emotional and social care needs. Due to target population specifications, eServices was designed to be as simple and accessible as possible in order to remove technological barriers. One of eServices major features is to detect imminent danger situations, act accordingly. It also collects the data from the sensors, location routines and from the interactions between the elderlies and the provided services to detect behavior deviations in order to act preventively. The platform was tested by seniors in real scenario. The experimental results demonstrated that the proposed platform was well accepted and easy to use by seniors, which demonstrated enthusiasm and interest in daily bases use.
- INTU-AI: Digitalization of Police Interrogation Supported by Artificial IntelligencePublication . Garcia, José António; Grilo, Carlos; Domingues, Patrício; Miragaia, RolandoTraditional police interrogation processes remain largely time-consuming and reliant on substantial human effort for both analysis and documentation. Intuition Artificial Intelligence (INTU-AI) is a Windows application designed to digitalize the administrative workflow associated with police interrogations, while enhancing procedural efficiency through the integration of AI-driven emotion recognition models. The system employs a multimodal approach that captures and analyzes emotional states using three primary vectors: Facial Expression Recognition (FER), Speech Emotion Recognition (SER), and Text-based Emotion Analysis (TEA). This triangulated methodology aims to identify emotional inconsistencies and detect potential suppression or concealment of affective responses by interviewees. INTU-AI serves as a decision-support tool rather than a replacement for human judgment. By automating bureaucratic tasks, it allows investigators to focus on critical aspects of the interrogation process. The system was validated in practical training sessions with inspectors and with a 12-question questionnaire. The results indicate a strong acceptance of the system in terms of its usability, existing functionalities, practical utility of the program, user experience, and open-ended qualitative responses.
- A maximum independent set approach for collusion detection in voting poolsPublication . Araujo, Filipe; Farinha, Jorge; Domingues, Patrício; Silaghi, Gheorghe Cosmin; Kondo, DerrickFrom agreement problems to replicated software execution, we frequently find scenarios with voting pools. Unfortunately, Byzantine adversaries can join and collude to distort the results of an election. We address the problem of detecting these colluders, in scenarios where they repeatedly participate in voting decisions. We investigate different malicious strategies, such as naïve or colluding attacks, with fixed identifiers or in whitewashing attacks. Using a graph-theoretic approach, we frame collusion detection as a problem of identifying maximum independent sets. We then propose several new graph-based methods and show, via analysis and simulations, their effectiveness and practical applicability for collusion detection.